Machine Learning and Optimization

In this paper, we develop a new model for recognizing human actions.

In this paper, we propose a structured kernel machine approach to treat object detection and pose estimation jointly in a mutually beneficial way.

We propose a hierarchical generalization of the ddCRP which clusters data within groups based on distances between data items, and couples clusters across groups via distances based on aggregate properties of these local clusters.

We propose a new weakly-supervised structured learning approach for recognition and spatio-temporal localization of actions in video.

This work reports the first experimental demonstration of CIT from a vertical magnetic dipole (VMD) in remote sensing and position tracking applications.

In this paper, we propose a method to overcome these issues by representing team behavior via play-segments, which are spatio-temporal descriptions of ball movement over fixed windows of time.

In this paper, we use ball and player tracking data from STATS SportsVU from the 2012-2013 NBA season to analyze offensive and defensive formations of teams.

We address the problem of planning collision-free paths for multiple agents using optimization methods known as proximal algorithms.

In this study, we explored the impact of a co-located sidekick on child-robot interaction. We examined child behaviors while interacting with an expressive furniture robot and his robot lamp sidekick.

We propose an unsupervised EM-style joint inference algorithm with a probabilistic CRF that models identity and role assignments for all detected faces, along with associated pairwise relationships between them.